NEAug 20, 2012
Learning sparse messages in networks of neural cliques
arXiv:1208.4009v111 citations
Originality Synthesis-oriented
AI Analysis
This work addresses memory efficiency in neural networks for applications in AI and neuroscience, but it appears incremental as an extension of an existing method.
The authors tackled the problem of learning sparse messages in neural networks by extending a binary neural network to achieve high memory efficiency and large numbers of messages, with simulation results demonstrating its effectiveness.
An extension to a recently introduced binary neural network is proposed in order to allow the learning of sparse messages, in large numbers and with high memory efficiency. This new network is justified both in biological and informational terms. The learning and retrieval rules are detailed and illustrated by various simulation results.